In this post, I will investigate the effects that ad spending can have on the outcome of elections on a district by district level. Using my current model, I will incorporate data found on the FEC’s website to approximate ad spend on a campaign district level. Using how much money was spent will be my variable, though I am aware that some scholarship says that number of ads rather than money spent is also a good indicator. I will also run tests on that next week. I will also be using data from the 2018 midterm to build my predictive model.
Political scientists Gregory A Huber and Kevin Arceneaux have noted in their research that most districts tend to be non-competitive. They also argue that “Advertising does a little to inform, next to nothing to mobilize, and a great deal to persuade potential voters.” A key takeaway from their work is how campaign advertising can do a great deal to persuade potential voters to candidates. Therefore, for undecided voters, it is highly likely that receiving more of a certain candidates ads more often than another will increase the likelihood of that voter to vote for the candidate which they saw more ads for.
This has pretty important implications and would be useful to include into our model and test whether or not the amount campaigns spend on ads and air space can affect our model’s prediction and accuracy for both 2018 and the upcoming 2022 election.
My current model from Week 4 included incumbency, economic factors, and basic polling. Currently, the R squared for this model is at .73, a fair amount.
| Observations | 438 |
| Dependent variable | DemVotesMajorPercent |
| Type | OLS linear regression |
| F(3,434) | 393.44 |
| R² | 0.73 |
| Adj. R² | 0.73 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 79.51 | 3.89 | 20.43 | 0.00 |
| avg | -6.73 | 0.20 | -34.06 | 0.00 |
| Unemployed_prct | -0.10 | 0.98 | -0.11 | 0.92 |
| winner_candidate_incIncumbent | 3.28 | 1.28 | 2.56 | 0.01 |
| Standard errors: OLS |
My new local model that includes district level data on polling, incumbency, and local employment data is much more accurate than before. However, the R-squared of 0.73 which is the highest so far only when I use DemMajorVotePct as my outcome variable. When I do DemSeats(which my previous models used) as my outcome variable, I get a lower R-squared of 0.50 exactly.
Below is a plot of the actual outcomes from the 2018 election from which im pulling my predictive modeling data from.
Now I have to add the variable of ad spending on a local level. I’ve gone ahead and downloaded data from the FEC for 2018 election spending data. This isn’t exactly the ad spend per campaign but I am using it as a proxy by making the assumption that the more money a particular race / candidate has overall translates to how much it is spending in ads.
| Observations | 448 |
| Dependent variable | DemVotesMajorPercent |
| Type | OLS linear regression |
| F(4,443) | 312.97 |
| R² | 0.74 |
| Adj. R² | 0.74 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 77.96 | 3.42 | 22.77 | 0.00 |
| avg | -6.73 | 0.19 | -35.15 | 0.00 |
| Unemployed_prct | 0.35 | 0.89 | 0.39 | 0.70 |
| winner_candidate_incIncumbent | 4.96 | 1.04 | 4.79 | 0.00 |
| Receipts | -0.00 | 0.00 | -3.02 | 0.00 |
| Standard errors: OLS |
Bafumi, J., Erikson, R., & Wlezien, C. (2018). Forecasting the 2018 Midterm Election using National Polls and District Information. PS: Political Science & Politics, 51(S1), 7-11. doi:10.1017/S1049096518001579
Ballotpedia. (2018). United States House of Representatives elections, 2018. https://ballotpedia.org/United_States_House_of_Representatives_elections,_2018
Ballotpedia. (2022). United States Congress elections, 2022. https://ballotpedia.org/United_States_Congress_elections,_2022
Congressional candidate data summary tables - FEC.gov. (2022). Retrieved 16 October 2022, from https://www.fec.gov/campaign-finance-data/congressional-candidate-data-summary-tables/?year=2022&segment=18
Cook Political Report. (2022). PVI Map and District List. https://www.cookpolitical.com/cook-pvi/2022-partisan-voting-index/district-map-and-list
Gerber, A.S., Gimpel, J. G., Green, D. P., & Shaw, D. R. (2011). How Large and Long-lasting Are the Persuasive Effects of Televised Campaign Ads? Results from a Randomized Field Experiment. American Political Science Review, 105(1), 135–150. https://doi.org/10.1017/S000305541000047X
Wesleyan Media Project. (2022, October 6). Democrats Out-Pacing GOP in Senate Races. https://mediaproject.wesleyan.edu/releases-100622/